Obesity is a major risk factor for metabolic disorders. Obesit typically leads to accumulation of dysfunctional white adipose tissue (WAT), which further causes metabolic dysregulation with elevated circulation of fatty acids and increased secretion of proinflammatory adipokines. The discovery of fat burning brown adipose tissue (BAT) in humans has raised the exciting possibility of BAT may be targeted as a novel method to treat obesity and metablic diseases. Thiazolidinediones (TZDs) have a function to convert WAT into a brownlike state. Beside, TZDs have been used as a remedy for diabetes. But the clinical use of TZDs has been limited because of the safety concerns such as potential cardiovascular risks. Understanding the mechanism will identify efficacious but lower risk drug targets for the metabolic disorders. TZDs act by activatin PPAR? (peroxisome proliferator-activated receptor ?). However, our understanding about the targets f PPAR? and other cofactors is limited. To understand the role of TZDs and further study the browning effect, we propose to develop a novel algorithm to predict long-range promoter-enhancer interaction and construct a transcriptional network. To predict the long-range interactions, we will employ a machine learning algorithm that uses the enhancer RNA (eRNA) levels and gene transcription levels obtained from global run-on sequencing (GROseq) data. GROseq is a useful dataset to predict long-range interactions, as the eRNA levels highly correlate with the gene transcription level. Applyingthe obtained interactions to the known binding sites of TFs including PPAR?, GR, C/EBP, SMRT, and RXR, we will construct a comprehensive TF-gene network. The predicted interaction provides a useful environment to study gene regulation of TZDs. We will study how the distance, relative position, an the combination of multiple TF binding sites affect gene expression. We will also investigate the browning effects by TZDs by including BAT-specific TF binding data in the network. The transcriptioal rule of the BAT-specific binding information, in combination with other TFs, will be analyzed from he TF-gene network. As a whole, these studies use an innovative and creative approach to integrate various types of data to study gene regulation of TZDs. By reprocessing complex genomic datasets ino a comprehensive regulatory network, the proposed algorithm provides a unique view in analyzing the regulatory rules of the browning effect, which will greatly enhance our understanding about the gen regulation of TZDs and identify potential therapeutic targets for diabetes.